Text Classification
Transformers
PyTorch
Safetensors
English
roberta
cryptocurrency
crypto
BERT
sentiment classification
NLP
bitcoin
ethereum
shib
social media
sentiment analysis
cryptocurrency sentiment analysis
text-embeddings-inference
Instructions to use ElKulako/cryptobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElKulako/cryptobert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ElKulako/cryptobert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ElKulako/cryptobert") model = AutoModelForSequenceClassification.from_pretrained("ElKulako/cryptobert", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -2
config.json
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{
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"_name_or_path": "/
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"architectures": [
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"RobertaForMaskedLM"
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],
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"torch_dtype": "float32",
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"transformers_version": "4.20.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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}
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{
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"_name_or_path": "ElKulako/CryptoBERT",
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"architectures": [
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"RobertaForMaskedLM"
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],
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"torch_dtype": "float32",
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"transformers_version": "4.20.0",
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"type_vocab_size": 1,
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"vocab_size": 50265
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}
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